Initialization of Lagrangian Multipliers in SVM using SMO (sequential minimal optimization)

Question

I came across a paper that implemented SVM using SMO. I planned to implement SVR (support vector regression) on the basis of it, using SMO. But I'm stuck. I want to ask how the initial values of lagrangian parameters are generated? Are they generated using a random function. Because I came across several implementation and there was no such notion of how initial values are generated.

Answer 1

Initial parameters can be taken random and SVR will eventually evolve with optimal ones. The second order derivative is guaranteed to be positive in SVR but in SVM it may not always support optimization.